code: def test_diff_correct(self, capsys):
      class ModelA(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.a = Parameter(torch.tensor([1.0, 0.0, 0.0]))
              self.b = Parameter(torch.tensor([1.0, 0.0, 0.0]))
              self.c = Parameter(torch.tensor([1.0, 0.0, 0.0]))
              self.e = Parameter(torch.tensor([1.0, 0.0, 0.0]))
  
      class ModelB(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.a = Parameter(torch.tensor([1.0, 0.0, 0.0]))
              self.b = Parameter(torch.tensor([1.0, 0.0, 0.0, 0.0]))
              self.d = Parameter(torch.tensor([1.0, 0.0, 0.0]))
              self.e = Parameter(torch.tensor([1.0, 0.0, 1.0]))
  
      model_a = ModelA()
      model_b = ModelB()
  
      torch.save(
          model_a.state_dict(), self.TEST_DIR / "checkpoint_a.pt"
      )
      torch.save(
          model_b.state_dict(), self.TEST_DIR / "checkpoint_b.pt"
      )
      sys.argv = [
          "allennlp",
          "diff",
          str(self.TEST_DIR / "checkpoint_a.pt"),
          str(self.TEST_DIR / "checkpoint_b.pt"),
      ]
      main()
      captured = capsys.readouterr()
      assert (
          _clean_output(captured.out)
          == """
   a, shape = (3,)
  -b, shape = (3,)
  -c, shape = (3,)
  +b, shape = (4,)
  +d, shape = (3,)
  !e, shape = (3,), distance = 0.5774
          """.strip()
      )
      # NOTE: the difference value here of for 'e' of 0.5774 is currently
      # calculated at the square root of the mean squared difference between 'e'
      # in 'model_a' and 'e' in 'model_b':
      # sqrt( (0^2 + 0^2 + 1^2) / 3 ) = sqrt( 1/3 ) = 0.5774
  
      # Now call again with a higher theshold.
      sys.argv = [
          "allennlp",
          "diff",
          str(self.TEST_DIR / "checkpoint_a.pt"),
          str(self.TEST_DIR / "checkpoint_b.pt"),
          "--threshold",
          "0.6",
      ]
      main()
      captured = capsys.readouterr()
      assert (
          _clean_output(captured.out)
          == """
   a, shape = (3,)
  -b, shape = (3,)
  -c, shape = (3,)
  +b, shape = (4,)
  +d, shape = (3,)
   e, shape = (3,)
          """.strip()
      )
  
      # And call a third time with the same threshold but a higher scale.
      sys.argv = [
          "allennlp",
          "diff",
          str(self.TEST_DIR / "checkpoint_a.pt"),
          str(self.TEST_DIR / "checkpoint_b.pt"),
          "--threshold",
          "0.6",
          "--scale",
          "10.0",
      ]
      main()
      captured = capsys.readouterr()
      assert (
          _clean_output(captured.out)
          == """
   a, shape = (3,)
  -b, shape = (3,)
  -c, shape = (3,)
  +b, shape = (4,)
  +d, shape = (3,)
  !e, shape = (3,), distance = 5.7735
          """.strip()
      )
  
file_path: allennlp/tests/commands/diff_test.py
imports: import sys
  import torch
  from torch.nn import Parameter
  from allennlp.commands import main
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 3 for calling the TestDiffCommand obj's underlying member method code with example usage: obj.test_diff_correct(capsys) without return types